Segmentation and analysis of blood vessels for medical imaging processes is generally known. It helps in the diagnosis and representation of vessel illnesses, OP preparation or blood flow simulations. For this purpose, imaging methods from nuclear magnetic resonance imaging and computed tomography are used which provide for a two- or three-dimensional noninvasive angiography of the vessel system.
In these angiographic methods, the vessels are in most cases additionally rendered particularly easily visible by the injection of suitable contrast agents into the circulatory system of the patient. The coronary arteries are of particular interest in this context. Their segmentation and enhancement from contrast-producing CT or MR data is decisive for determining the degree of a vessel stenosis due to soft or calcified plaques, which represents the main reason for a cardiac infarction.
In the prior art, a number of methods for vessel segmentation from two- or three-dimensional medical imaging has been proposed. Typical methods with threshold value formation or so-called “region growing” are mathematically efficient but rely completely on the measured intensity values of the image voxels. As soon as there is no further information about the geometry, these methods suffer from faulty segmentation of other contrasted regions such as the heart ventricles, bones or the aorta.
More highly developed methods are based on deformable models. However, geometric models such as generalized cylinders are normally not sufficient for being able to adequately represent complex tree structures.
Although statistical methods with preformed models are able to represent complex structures, they need prior intensive training of a large representative database. Abnormal vessel branching which, as a rule, represents the most critical places in the segmentation algorithm are frequently wrongly segmented here. Furthermore, although model-based methods are more robust against noise in the image data, many of them cannot be implemented three-dimensionally since the mathematical and operating effort increases to an extreme extent.